Figure REF  illustrates the FP predictor can be used to directly predict the performance of INT8 networks, indicating that DONNA search spaces can indeed be trivially extended to include INT8 precision. Fig. REF (left) shows FP train and test data for the accuracy predictor model. Fig. REF (right) shows FP train and INT8 test data using the same FP accuracy predictor.
Formally, we compare the performance of this predictor on the FP and INT8 test set by comparing the achieved prediction MSE and Kendal-Tau (KT) [1]}. We can observe that there are no outliers when using the predictor to predict the accuracy of INT8 networks. MSE for the FP test set is 0.13 and 0.34 for the INT8 test set. MSE for INT8 is higher because of the noise introduced by the quantization process. Nonetheless the KT-ranking is 0.85 for FP test set and 0.86 for the INT8 test set demonstrating that the accuracy predictor can be used for INT8-quantized models.
